1. Field of the Invention
The present invention relates to the computer modeling of a potentially dynamic anatomical structure in a given image volume(s). This allows for the measurement of the geometry of the underlying object at a specific point in time or over a set of time periods.
2. Description of Related Art
Temporal variation in organ morphology is frequently of interest to physicians and surgeons. In the heart, for example, the change in blood pool volume (i.e., volume difference) of the left ventricle over the cardiac cycle (i.e., the operational phases of the heart) is used to diagnose dysfunction and determine a course of treatment. Changes in the strain tensor of cardiac tissue indicate problems in underlying material properties (i.e., infarction of damaged tissue). Similarly, differential lung capacity is an effective determinant for assessing pulmonary disease. In the brain, swelling in response to injury may be tracked over a period of time to provide an indicator of severity of head trauma. In another example, disease progression such as cirrhosis is reflected in the shape of evolution of the liver (i.e., the status of the anatomical organ). Indeed, the history of tumor volume is a value scrutinized closely by doctors. For replacement actions (i.e., hip replacement) the full degree of motion of the skeletal structure must be recovered.
A method for segmenting objects from 3D images using a 3D deformable surface which was made up of a series of 2D planar curves is proposed. However, this model is not cohesive in terms of being 3-D and is more a propagation of 2-D contours in space followed by a stitching together of the contours. In addition, the 2D planar curves are not recovered via optimal active contours (xe2x80x9coptimalxe2x80x9d meaning that the energy function describing the contour is globally minimized). Instead the proposed method employs snakes and relies on balloon forces to explore crevices. The problem with balloon forces is that the snake might leak where the image boundaries are not well defined. 2D deformable surfaces have also been applied to segmentation but the approaches have not been xe2x80x9coptimalxe2x80x9d. For 1D contours, optimality is a well understood concept. How this concept might be extended to 2D surfaces still presents difficulty.
Direct application of 3D models to 3D image volumes has also met with mixed success. Again, describing deep crevices becomes a problem. Some proposed methods fit parallel sets of 2D contours to recover a 3D object. Once the fit is settled the methods repeat the process from an orthogonal direction using the results of the previous iteration as a starting point. However, these methods employ balloon forces to fit the 2Dcontours and their result is not a coherent 3-D surface model. In addition, these methods are applied on relatively simple synthetic shapes. Region growing techniques are also used for segmentation. However, while these techniques are often effective they suffer from bleeding in areas where the object boundary is not well defined. In addition, these techniques do not result in a geometric description of the object, rather they result in a collection of voxels. A voxel is a unit of graphic information that defines a point in three dimensional space (in other words, a volume pixel).
Segmentation via the propagation of 2D active contours (i.e., using the result from a previous slice as the starting point for a segmentation of the current slice) is problematic. A change in an object""s circumference in a slice might be due to a change in the radius of the object under recovery, or it might be due to a change in direction of the path taken by the object in space. Determining if a change in circumference or direction has occurred is essential for selecting an appropriate starting point for segmentation in the slices to follow. Two-dimensional active contours lack the global properties necessary to account for these instances.
3-D models are powerful tools. They can provide detailed description of an object. It is difficult, however, to directly employ 3-D models in the segmentation process since they are not guaranteed to locate the optimal boundaries in cross-sectional slices. Propagating 2D active contours from slice to slice, on the other hand, to delineate an object""s boundaries is effective but encounters problems when an object""s shape dramatically changes such as in areas of high curvature.
A cooperative framework to exploit the positive aspects of both 3D model and 2D active contour approaches for segmentation and recovery is advantageous. In this framework, a default model shape, positioned in the data would provide starting points for a set of 2D segmentations (refinements) performed by active contours. The same model is fitted to the results of the segmentation.
Therefore, a need exists for a general cooperative approach for segmenting objects from 3D image volumes which exploits the positive aspects of both 2D and 3D traditional approaches.
The present invention relates to a system of modeling a three dimensional target object which is represented by a plurality of cross-sectional images in order to provide a representative corresponding three dimensional model. The invention selects an initial model from a plurality of available initial models. This selection involves identifying an initial model based on physical similarity to the target object and then superimposing an initial model upon the target object, for each of the plurality of cross-sectional images. A determination is then made of an intersection contour of the initial model and a cross-sectional image of the target object and the determined intersection contour is refined in order to more closely delineate the target object. By sub-sampling points which represent the refined determined intersection contour, the invention obtains a sub-sampled contour dataset. The initial model is then adjusted towards the sub-samples contour to obtain a representative three dimensional model of the target object.